Botanists typically spend a large amount of time in the field collecting samples for their research. Historically botanists have relied upon textbooks and compendiums of plant samples to correctly identify plant varieties from leaf and flower samples. The identification of plant varieties from leaf samples is based upon a close examination of leaf venation patterns, the configuration of leaf shapes and other identifying features and matching observed but unknown features with known specimens, photographs, illustrations or descriptions.
Recent developments in computer and electronic technological fields have meant that access to digitally stored data is more readily available. Further, computer chip processing speeds and memory storage capacities have increased significantly such that it is now possible to store within a relatively small sized device a large volume of digital data or to access remotely stored data using wireless communication means. The integration of such technology with plant identification and classification algorithms is now possible and desirable as a means of facilitating the ease by which botanists might accurately identify plant samples whilst engaged in field work.
It would be advantageous to develop an apparatus and method for correctly identifying plant varieties whilst in the field based upon an examination of leaf samples which employs a suitably designed and configured electronic device. This could greatly improve the productivity of botanists engaged in field research and reduce the overall costs of undertaking such research. Such an apparatus and method may have other uses outside the field of botanical research, for example, in correctly identifying a plant type in the case of a patient experiencing an allergic reaction to a plant which may potentially be life threatening. In such circumstances the apparatus and method for identifying plant varieties may be very valuable in preserving life or in accurately diagnosing a particular medical condition and prescribing a suitable prophylaxis or remedy.
There are a number of prior art apparatuses and methods for identifying plants through leaf venation. On the “Leafsnap” website for example, there is described an application suitable for use on an i-Phone for identifying leaves which involves taking photographs of leaf samples and matching the photograph of the sample to existing photographs stored within a database. The application compares the outline of the sample leaf or flower which is not sufficiently accurate to enable correct identification of plant species to the degree required of a botanist. The application is suitable for hobbyists.
US Patent 20080059076 describes a method for classifying leaves utilizing venation features. The method includes taking a sample venation image using a Curvature Scale Space Corner Detection Algorithm. The image is then treated to thicken the venation and increase the contrast through the retrieval unit. Canny Edge Detection technology is then applied to detect the feature, branching and end points where the calculated curvature angle is a local maximum. The distribution of the feature points of the extracted venation is calculated by applying a Parzen Window non-parametric estimation method.
Existing methods of identifying plant varieties from leaf and flower samples including those referred to above however suffer from a lack of accuracy and ease of use, particularly in field situations. The method described in US 20080059076, for instance, focuses on the process of categorising leaf venation into 4 categories: pinnate, first parallel, second parallel and palmate. It assumes that the image has been captured and does not make mention as to how the exact type of leaf is determined once it has been classified into one of the four categories employed. There is a lack of cross-reference to other characteristic features of the samples examined and no overriding means of enhancing the accuracy of data captured.
The use of photographic devices to capture leaf images is known. The use of scanning devices to capture leaf images for the purpose of identification is also known known however hitherto apparatus and methods for enabling identification of plant varieties from leaf samples are inefficient and subject to a significant degree of error so as to make their use commercially unviable. Particularly, images produced by known apparatus and methods are of insufficient contrast, detail and clarity and do not produce image data which can be readily and effectively applied to assist identification of a subject plant species. It would be advantageous to provide an apparatus and method for identifying plant varieties from leaf samples which produced enhanced images for analysis in order to alleviate the large margin for error in identifying the leaf species using available apparatus and techniques. Such an apparatus and method would greatly reduce the time necessary to accurately identify a plant variety from a leaf sample, and image produced therefrom and would also significantly reduce the financial costs of providing such a service.
It would be advantageous to provide an apparatus and method for identifying plant varieties from leaf samples which overcomes at least some of the problems of prior art devices and which provided for greater accuracy in identification of samples.
Accordingly there is provided an apparatus for identifying plant varieties from leaf samples taken whilst in the field comprising:
a scanning device having an image sensor and an LED backlight so as to enable a detailed image of a sample to be recorded digitally;
a computer for uploading the image for analysis;
a computer program which allocates user prescribed parameters such as leaf venation, leaf shape, base position and shape and leaf curvature to the image;
utilising the data produced by the computer program and applying an algorithm to it for matching the data against a database of plant varieties to determine the highest match probability.
In some preferred embodiments the apparatus is a hand-held or laptop computer.
There is provided a method for identifying plant varieties from leaf samples taken whilst in the field including the steps:    (i) acquiring a scanned image of a leaf sample using an apparatus comprising an LED back lit scanning device having an image sensor and an LED backlight and a computer for uploading the image for analysis;    (ii) applying an image manipulation algorithm to the scanned image to enhance venation data recorded;    (iii) producing a venation line drawing;    (iv) cross-referencing venation line drawing data with a set of identification data;    (v) comparing venation and identification data with known samples stored in a database;    (vi) choosing and displaying the most probable match for plant variety according to the sample analysis.
There is also provided a method for identifying plant varieties from leaf samples taken whilst in the field including the steps:    (i) harvesting a leaf sample;    (ii) taking a photographic image of the leaf sample using a back lit scanning device having an image sensor and an LED backlight;    (iii) extracting a set of identification parameters from the photographic image including leaf venation, leaf shape, base position and shape and leaf curvature;    (iv) applying an identification algorithm to the extracted identification parameters;    (v) presenting the results of the algorithmic analysis on a screen of a computer or hand held device;    (vi) comparing the sample with illustrations of known leaf samples;    (vii) choosing and displaying the most probable match for plant variety according to the sample analysis.